Traum, David


Listen to My Body: Does Making Friends Help Influence People?

AAAI Conferences

We investigate the effect of relational dialogue on creating rapport and exerting social influence in human-robot conversation, by comparing interactions with and without a relational component, and with different agent types. Human participants interact with two agents — a Nao robot and a virtual human — in four dialogue scenarios: one involving building familiarity, and three involving sharing information and persuasion in item-ranking tasks. Results show that both agents influence human decision-making; people prefer interacting with the robot, feel higher rapport with the robot, and believe the robot has more influence; and that objective influence of the agent on the person is increased by building familiarity, but is not significantly different between the agents.



Virtual Humans for Learning

AI Magazine

Virtual humans are computer-generated characters designed to look and behave like real people. Studies have shown that virtual humans can mimic many of the social effects that one finds in human-human interactions such as creating rapport, and people respond to virtual humans in ways that are similar to how they respond to real people. We believe that virtual humans represent a new metaphor for interacting with computers, one in which working with a computer becomes much like interacting with a person and this can bring social elements to the interaction that are not easily supported with conventional interfaces. The second SimCoach, uses an empathetic virtual human to provide veterans and their families with information about PTSD and depression.


Virtual Humans for Learning

AI Magazine

Virtual humans are computer-generated characters designed to look and behave like real people. Studies have shown that virtual humans can mimic many of the social effects that one finds in human-human interactions such as creating rapport, and people respond to virtual humans in ways that are similar to how they respond to real people. We believe that virtual humans represent a new metaphor for interacting with computers, one in which working with a computer becomes much like interacting with a person and this can bring social elements to the interaction that are not easily supported with conventional interfaces. We present two systems that embody these ideas. The first, the Twins are virtual docents in the Museum of Science, Boston, designed to engage visitors and raise their awareness and knowledge of science. The second SimCoach, uses an empathetic virtual human to provide veterans and their families with information about PTSD and depression.


Augmenting Conversational Characters with Generated Question-Answer Pairs

AAAI Conferences

We take a conversational character trained on a set of linked question-answer pairs authored by hand, and augment its training data by adding sets of question-answer pairs which are generated automatically from texts on different topics. The augmented characters can answer questions about the new topics, at the cost of some performance loss on questions about the topics that the original character was trained to answer.


NPCEditor: Creating Virtual Human Dialogue Using Information Retrieval Techniques

AI Magazine

NPCEditor is a system for building a natural language processing component for virtual humans capable of engaging a user in spoken dialog on a limited domain. It uses statistical language classification technology for mapping from a user’s text input to system responses. NPCEditor provides a user-friendly editor for creating effective virtual humans quickly. It has been deployed as a part of various virtual human systems in several applications.


Improving Spoken Dialogue Understanding Using Phonetic Mixture Models

AAAI Conferences

Augmenting word tokens with a phonetic representation, derived from a dictionary, improves the performance of a Natural Language Understanding component that interprets speech recognizer output: we observed a 5% to 7% reduction in errors across a wide range of response return rates. The best performance comes from mixture models incorporating both word and phone features. Since the phonetic representation is derived from a dictionary, the method can be applied easily without the need for integration with a specific speech recognizer. The method has similarities with autonomous (or bottom-up) psychological models of lexical access, where contextual information is not integrated at the stage of auditory perception but rather later.


Evaluating Conversational Characters Created through Question Generation

AAAI Conferences

Question generation tools can be used to extract a question-answer database from text articles. We investigate how suitable this technique is for giving domain-specific knowledge to conversational characters. We tested these characters by collecting questions and answers from naive participants, running the questions through the character, and comparing the system responses to the participant answers. Characters gave a full or partial answer to 53% of the user questions which had an answer available in the source text, and 43% of all questions asked. Performance was better for questions asked after the user had read the source text, and also varied by question type: the best results were answers to who questions, while answers to yes/no questions were among the poorer performers. The results show that question generation is a promising method for creating a question answering conversational character from an existing text.


Practical Language Processing for Virtual Humans

AAAI Conferences

NPCEditor is a system for building a natural language processing component for virtual humans capable of engaging a user in spoken dialog on a limited domain. It uses a statistical language classification technology for mapping from user's text input to system responses. NPCEditor provides a user-friendly editor for creating effective virtual humans quickly. It has been deployed as a part of various virtual human systems in several applications.


Improving a Virtual Human Using a Model of Degrees of Grounding

AAAI Conferences

We describe the Degrees of Grounding model, which tracks the extent to which material has reached mutual belief in a dialogue, and conduct experiments in which the model is used to manage grounding behavior in spoken dialogues with a virtual human.  We show that the model produces improvements in virtual human performance as measured by post-session questionnaires.